
import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    DebertaForMaskedLM,
	    DebertaForQuestionAnswering,
	    DebertaForSequenceClassification,
	    DebertaForTokenClassification,
	    DebertaModel,
	)
	from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	def __init__(			self						,       snake_case						,       snake_case=13						,       snake_case=7						,       snake_case=True						,       snake_case=True						,       snake_case=True						,       snake_case=True						,       snake_case=99						,       snake_case=32						,       snake_case=5						,       snake_case=4						,       snake_case=37						,       snake_case="gelu"						,       snake_case=0.1						,       snake_case=0.1						,       snake_case=512						,       snake_case=16						,       snake_case=2						,       snake_case=0.02						,       snake_case=False						,       snake_case=True						,       snake_case="None"						,       snake_case=3						,       snake_case=4						,       snake_case=None						,       ):
		lowercase	    =       parent
		lowercase	    =       batch_size
		lowercase	    =       seq_length
		lowercase	    =       is_training
		lowercase	    =       use_input_mask
		lowercase	    =       use_token_type_ids
		lowercase	    =       use_labels
		lowercase	    =       vocab_size
		lowercase	    =       hidden_size
		lowercase	    =       num_hidden_layers
		lowercase	    =       num_attention_heads
		lowercase	    =       intermediate_size
		lowercase	    =       hidden_act
		lowercase	    =       hidden_dropout_prob
		lowercase	    =       attention_probs_dropout_prob
		lowercase	    =       max_position_embeddings
		lowercase	    =       type_vocab_size
		lowercase	    =       type_sequence_label_size
		lowercase	    =       initializer_range
		lowercase	    =       num_labels
		lowercase	    =       num_choices
		lowercase	    =       relative_attention
		lowercase	    =       position_biased_input
		lowercase	    =       pos_att_type
		lowercase	    =       scope





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size			)

		lowercase	    =       None
		if self.use_input_mask:
			lowercase	    =       ids_tensor([self.batch_size, self.seq_length]						,       vocab_size=2			)

		lowercase	    =       None
		if self.use_token_type_ids:
			lowercase	    =       ids_tensor([self.batch_size, self.seq_length]						,       self.type_vocab_size			)

		lowercase	    =       None
		lowercase	    =       None
		lowercase	    =       None
		if self.use_labels:
			lowercase	    =       ids_tensor([self.batch_size]						,       self.type_sequence_label_size			)
			lowercase	    =       ids_tensor([self.batch_size, self.seq_length]						,       self.num_labels			)
			lowercase	    =       ids_tensor([self.batch_size]						,       self.num_choices			)

		lowercase	    =       self.get_config()

		return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





	def       SCREAMING_SNAKE_CASE__		(			self			):
		return DebertaConfig(
		    vocab_size=self.vocab_size						,       hidden_size=self.hidden_size						,       num_hidden_layers=self.num_hidden_layers						,       num_attention_heads=self.num_attention_heads						,       intermediate_size=self.intermediate_size						,       hidden_act=self.hidden_act						,       hidden_dropout_prob=self.hidden_dropout_prob						,       attention_probs_dropout_prob=self.attention_probs_dropout_prob						,       max_position_embeddings=self.max_position_embeddings						,       type_vocab_size=self.type_vocab_size						,       initializer_range=self.initializer_range						,       relative_attention=self.relative_attention						,       position_biased_input=self.position_biased_input						,       pos_att_type=self.pos_att_type						,       )





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.get_config()
		lowercase	    =       300
		return config





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		self.parent.assertListEqual(list(result.loss.size()			)						,       []			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case			):
		lowercase	    =       DebertaModel(config=snake_case			)
		model.to(snake_case			)
		model.eval()
		lowercase	    =       model(snake_case						,       attention_mask=snake_case						,       token_type_ids=snake_case			)[0]
		lowercase	    =       model(snake_case						,       token_type_ids=snake_case			)[0]
		lowercase	    =       model(snake_case			)[0]

		self.parent.assertListEqual(list(sequence_output.size()			)						,       [self.batch_size, self.seq_length, self.hidden_size]			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case			):
		lowercase	    =       DebertaForMaskedLM(config=snake_case			)
		model.to(snake_case			)
		model.eval()
		lowercase	    =       model(snake_case						,       attention_mask=snake_case						,       token_type_ids=snake_case						,       labels=snake_case			)

		self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.seq_length, self.vocab_size)			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case			):
		lowercase	    =       self.num_labels
		lowercase	    =       DebertaForSequenceClassification(snake_case			)
		model.to(snake_case			)
		model.eval()
		lowercase	    =       model(snake_case						,       attention_mask=snake_case						,       token_type_ids=snake_case						,       labels=snake_case			)
		self.parent.assertListEqual(list(result.logits.size()			)						,       [self.batch_size, self.num_labels]			)
		self.check_loss_output(snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case			):
		lowercase	    =       self.num_labels
		lowercase	    =       DebertaForTokenClassification(config=snake_case			)
		model.to(snake_case			)
		model.eval()
		lowercase	    =       model(snake_case						,       attention_mask=snake_case						,       token_type_ids=snake_case						,       labels=snake_case			)
		self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.seq_length, self.num_labels)			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case			):
		lowercase	    =       DebertaForQuestionAnswering(config=snake_case			)
		model.to(snake_case			)
		model.eval()
		lowercase	    =       model(
		    snake_case						,       attention_mask=snake_case						,       token_type_ids=snake_case						,       start_positions=snake_case						,       end_positions=snake_case						,       )
		self.parent.assertEqual(result.start_logits.shape						,       (self.batch_size, self.seq_length)			)
		self.parent.assertEqual(result.end_logits.shape						,       (self.batch_size, self.seq_length)			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.prepare_config_and_inputs()
		(
		    (
		    lowercase
		)    ,   (
		    lowercase
		)    ,   (
		    lowercase
		)    ,   (
		    lowercase
		)    ,   (
		    lowercase
		)    ,   (
		    lowercase
		)    ,   (
		    lowercase
		)    ,   
		)	    =       config_and_inputs
		lowercase	    =       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
		return config, inputs_dict







@require_torch
class 			A_  (     __lowerCamelCase ,      __lowerCamelCase ,      unittest.TestCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	List[Any]									=			(
	    (
	        DebertaModel,
	        DebertaForMaskedLM,
	        DebertaForSequenceClassification,
	        DebertaForTokenClassification,
	        DebertaForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	_UpperCamelCase	:	int									=			(
	    {
	        """feature-extraction""": DebertaModel,
	        """fill-mask""": DebertaForMaskedLM,
	        """question-answering""": DebertaForQuestionAnswering,
	        """text-classification""": DebertaForSequenceClassification,
	        """token-classification""": DebertaForTokenClassification,
	        """zero-shot""": DebertaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)

	_UpperCamelCase	:	Union[str, Any]									=			True
	_UpperCamelCase	:	Any									=			False
	_UpperCamelCase	:	Optional[Any]									=			False
	_UpperCamelCase	:	Union[str, Any]									=			False
	_UpperCamelCase	:	Optional[Any]									=			False





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       DebertaModelTester(self			)
		lowercase	    =       ConfigTester(self						,       config_class=snake_case						,       hidden_size=37			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		self.config_tester.run_common_tests()





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_deberta_model(*snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_deberta_for_sequence_classification(*snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_deberta_for_masked_lm(*snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_deberta_for_question_answering(*snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_deberta_for_token_classification(*snake_case			)





	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowercase	    =       DebertaModel.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)







@require_torch
@require_sentencepiece
@require_tokenizers
class 			A_  (     unittest.TestCase  ):




	'''simple docstring'''


	@unittest.skip(reason='Model not available yet'			)
	def       SCREAMING_SNAKE_CASE__		(			self			):
		pass





	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       DebertaModel.from_pretrained('microsoft/deberta-base'			)

		lowercase	    =       torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]			)
		lowercase	    =       torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
		with torch.no_grad():
			lowercase	    =       model(snake_case						,       attention_mask=snake_case			)[0]
		# compare the actual values for a slice.
		lowercase	    =       torch.tensor(
		    [[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]]			)
		self.assertTrue(torch.allclose(output[:, 1:4, 1:4]						,       snake_case						,       atol=1E-4			)						,       F'''{output[:, 1:4, 1:4]}'''			)




from math import factorial







class 					__lowercase  :





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Optional[int]:
														A      :					Union[str, Any]      		=					real
														if isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					str      		=					[1] * rank
														else:
																					A      :					Dict      		=					rank




							def __repr__(					self						)		->       List[str]:
														return (
														    f'{self.real}+'
														    f'{"+".join(str(__UpperCAmelCase						)+"E"+str(n+1						)for n,dual in enumerate(self.duals						)						)}'
														)




							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					List[Any]      		=					self.duals.copy()
														while cur[-1] == 0:
																					cur.pop(-1						)
														return Dual(self.real    ,						__UpperCAmelCase						)




							def __add__(					self    ,						__UpperCAmelCase						)		->       Any:
														if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					return Dual(self.real + other    ,						self.duals						)
														A      :					int      		=					self.duals.copy()
														A      :					Any      		=					other.duals.copy()
														if len(__UpperCAmelCase						) > len(__UpperCAmelCase						):
																					o_dual.extend([1] * (len(__UpperCAmelCase						) - len(__UpperCAmelCase						))						)
														elif len(__UpperCAmelCase						) < len(__UpperCAmelCase						):
																					s_dual.extend([1] * (len(__UpperCAmelCase						) - len(__UpperCAmelCase						))						)
														A      :					List[str]      		=					[]
														for i in range(len(__UpperCAmelCase						)						):
																					new_duals.append(s_dual[i] + o_dual[i]						)
														return Dual(self.real + other.real    ,						__UpperCAmelCase						)

							UpperCAmelCase_						:		int								=	__add__




							def __sub__(					self    ,						__UpperCAmelCase						)		->       List[str]:
														return self + other * -1




							def __mul__(					self    ,						__UpperCAmelCase						)		->       List[str]:
														if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					Union[str, Any]      		=					[]
																					for i in self.duals:
																												new_duals.append(i * other						)
																					return Dual(self.real * other    ,						__UpperCAmelCase						)
														A      :					Optional[Any]      		=					[0] * (len(self.duals						) + len(other.duals						) + 1)
														for i, item in enumerate(self.duals						):
																					for j, jtem in enumerate(other.duals						):
																												new_duals[i + j + 1] += item * jtem
														for k in range(len(self.duals						)						):
																					new_duals[k] += self.duals[k] * other.real
														for index in range(len(other.duals						)						):
																					new_duals[index] += other.duals[index] * self.real
														return Dual(self.real * other.real    ,						__UpperCAmelCase						)

							UpperCAmelCase_						:		int								=	__mul__




							def __truediv__(					self    ,						__UpperCAmelCase						)		->       Optional[int]:
														if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					Optional[Any]      		=					[]
																					for i in self.duals:
																												new_duals.append(i / other						)
																					return Dual(self.real / other    ,						__UpperCAmelCase						)
														raise ValueError




							def __floordiv__(					self    ,						__UpperCAmelCase						)		->       Union[str, Any]:
														if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					List[Any]      		=					[]
																					for i in self.duals:
																												new_duals.append(i // other						)
																					return Dual(self.real // other    ,						__UpperCAmelCase						)
														raise ValueError






							def __pow__(					self    ,						__UpperCAmelCase						)		->       Any:
														if n < 0 or isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					raise ValueError('''power must be a positive integer'''						)
														if n == 0:
																					return 1
														if n == 1:
																					return self
														A      :					Dict      		=					self
														for _ in range(n - 1						):
																					x *= self
														return x






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							if not callable(lowerCamelCase_		):
														raise ValueError('''differentiate() requires a function as input for func'''		)
							if not isinstance(lowerCamelCase_     ,				(float, int)		):
														raise ValueError('''differentiate() requires a float as input for position'''		)
							if not isinstance(lowerCamelCase_     ,				lowerCamelCase_		):
														raise ValueError('''differentiate() requires an int as input for order'''		)
							A      :					Any      		=					Dual(lowerCamelCase_     ,				1		)
							A      :					Dict      		=					func(lowerCamelCase_		)
							if order == 0:
														return result.real
							return result.duals[order - 1] * factorial(lowerCamelCase_		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






		def        snake_case__		(    lowerCamelCase_		):
									return y**2 * y**4

		print(differentiate(f, 9, 2))






